A multi-sample based method for identifying common CNVs in normal human genomic structure using high-resolution aCGH data.

ABSTRACT:

Background

It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample.

Methodology and principal findings

We

SUBMITTER: Park C 

PROVIDER: S-EPMC3205051 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature